Automated segmentation in medical image analysis is a challenging task that requires a
large amount of manually labeled data. However, most existing learning-based approaches …

Vision transformers (ViTs) have recently received explosive popularity, but their enormous
model sizes and training costs remain daunting. Conventional post-training pruning often …

With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging,
the training and inference of GNNs become increasingly expensive. Existing network weight …

The computer vision world has been re-gaining enthusiasm in various pre-trained models,
including both classical ImageNet supervised pre-training and recently emerged self …

The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …

There have been long-standing controversies and inconsistencies over the experiment
setup and criteria for identifying the" winning ticket" in literature. To reconcile such, we revisit …

The lottery ticket hypothesis (LTH) has shown that dense models contain highly sparse
subnetworks (ie, winning tickets) that can be trained in isolation to match full accuracy …

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical
subnetworks) for dense networks, that can be trained in isolation from random initialization …

Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally
on most samples, yet to produce manipulated results for inputs attached with a particular …

Large-scale pre-training has recently revolutionized vision-and-language (VL) research.
Models such as LXMERT and UNITER have significantly lifted the state of the art over a wide …